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Daily Peak-Valley Electric-Load Forecasting Based on an SSA-LSTM-RF Algorithm

Author

Listed:
  • Yaoying Wang

    (School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China)

  • Shudong Sun

    (School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China)

  • Zhiqiang Cai

    (School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China)

Abstract

In recent years, with the development of societies and economies, the demand for social electricity has further increased. The efficiency and accuracy of electric-load forecasting is an important guarantee for the safety and reliability of power system operation. With the sparrow search algorithm (SSA), long short-term memory (LSTM), and random forest (RF), this research proposes an SSA-LSTM-RF daily peak-valley forecasting model. First, this research uses the Pearson correlation coefficient and the random forest model to select features. Second, the forecasting model takes the target value, climate characteristics, time series characteristics, and historical trend characteristics as input to the LSTM network to obtain the daily-load peak and valley values. Third, the super parameters of the LSTM network are optimized by the SSA algorithm and the global optimal solution is obtained. Finally, the forecasted peak and valley values are input into the random forest as features to obtain the output of the peak-valley time. The forest value of the SSA-LSTM-RF model is good, and the fitting ability is also good. Through experimental comparison, it can be seen that the electric-load forecasting algorithm based on the SSA-LSTM-RF model has higher forecasting accuracy and provides ideal performance for electric-load forecasting with different time steps.

Suggested Citation

  • Yaoying Wang & Shudong Sun & Zhiqiang Cai, 2023. "Daily Peak-Valley Electric-Load Forecasting Based on an SSA-LSTM-RF Algorithm," Energies, MDPI, vol. 16(24), pages 1-21, December.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:7964-:d:1296476
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    References listed on IDEAS

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    1. Singh, Priyanka & Dwivedi, Pragya, 2018. "Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem," Applied Energy, Elsevier, vol. 217(C), pages 537-549.
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